{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Housekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Bunch of imports \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorstyle=dict(red_face = np.array([255,85,65])/255, red_edge = np.array([201,67,52])/255,\n",
    "                blue_face = np.array([49,115,255])/255, blue_edge = np.array([36,85,189])/255,\n",
    "                green_face= np.array([84,224,81])/255, green_edge= np.array([62,166,60])/255,\n",
    "                yellow_face=np.array([255,207,49])/255,yellow_edge=np.array([191,155,36])/255,\n",
    "                gray_face=np.array([169,169,169])/255,gray_edge=np.array([137,137,137])/255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shaking amplitde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vamp=np.array([0.5,0.25,0.15,0.375])\n",
    "g_V=4.24\n",
    "gamp=Vamp*g_V\n",
    "\n",
    "SoundAmp    = np.array([4.69,2.07,1.37,3.03])\n",
    "SoundAmp_err= np.array([0.22,0.25,0.18,0.2])\n",
    "\n",
    "Gamma    = np.array([4.69,3.60,4.42,4.87])\n",
    "Gamma_err= np.array([0.36,0.75,1.15,0.61])\n",
    "\n",
    "omega    = np.array([20.238,20.915,20.919,20.369])\n",
    "omega_err= np.array([0.214,0.417,0.668,0.346])\n",
    "\n",
    "\n",
    "def linfit(x,a): return a*x\n",
    "\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "popt,pocv=curve_fit(linfit,gamp,SoundAmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 675x180 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_line=np.linspace(0,2.5,100)\n",
    "\n",
    "fig,axs=plt.subplots(nrows=1,ncols=3,figsize=[6.75,1.8])\n",
    "axs[0].tick_params(axis='both',direction='in',top=0,right=0,bottom=1,labelsize=9,length=2)\n",
    "axs[1].tick_params(axis='both',direction='in',top=0,right=0,bottom=1,labelsize=9,length=2)\n",
    "axs[2].tick_params(axis='both',direction='in',top=0,right=0,bottom=1,labelsize=9,length=2)\n",
    "\n",
    "\n",
    "\n",
    "plt.sca(axs[0])\n",
    "plt.errorbar(gamp,SoundAmp,yerr=SoundAmp_err,linestyle='None',marker='o',markersize=3,\n",
    "            mec=colorstyle['blue_edge'],mfc=colorstyle['blue_face'],ecolor=colorstyle['blue_edge'])\n",
    "\n",
    "plt.plot(x_line,linfit(x_line,*popt),'--k',linewidth=0.75)\n",
    "plt.xlim([0,2.5])\n",
    "plt.ylim([0,5.3])\n",
    "plt.xlabel(r'$g$ ($h\\cdot \\mathrm{Hz}/\\mathrm{\\mu m}$)',fontsize=9)\n",
    "plt.ylabel(r'$\\Delta T$ (a.u.)',fontsize=9)\n",
    "\n",
    "\n",
    "plt.sca(axs[1])\n",
    "plt.errorbar(gamp,SoundAmp/gamp,yerr=SoundAmp_err/gamp,linestyle='None',marker='o',markersize=3,\n",
    "            mec=colorstyle['blue_edge'],mfc=colorstyle['blue_face'],ecolor=colorstyle['blue_edge'])\n",
    "plt.axhline(popt,linestyle='--',color='k',linewidth=0.75)\n",
    "plt.xlim([0,2.5])\n",
    "plt.xlabel(r'$g$ ($h\\cdot \\mathrm{Hz}/\\mathrm{\\mu m}$)',fontsize=9)\n",
    "plt.ylabel(r'$\\Delta T/g$ (a.u.)',fontsize=9)\n",
    "plt.ylim([0,2.7])\n",
    "\n",
    "plt.sca(axs[2])\n",
    "plt.errorbar(gamp,Gamma,yerr=Gamma_err,linestyle='None',marker='o',markersize=3,\n",
    "            mec=colorstyle['blue_edge'],mfc=colorstyle['blue_face'],ecolor=colorstyle['blue_edge'])\n",
    "plt.axhline(4.25,linestyle='--',color='k',linewidth=0.75)\n",
    "plt.xlim([0,2.5])\n",
    "plt.xlabel(r'$g$ ($h\\cdot \\mathrm{Hz}/\\mathrm{\\mu m}$)',fontsize=9)\n",
    "plt.ylabel(r'$\\Gamma/2\\pi$ (Hz)',fontsize=9)\n",
    "plt.ylim([0,6])\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "fig.savefig('FigS9.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_out=pd.DataFrame({'g(h Hz/um)':gamp,'DeltaT':SoundAmp,'DeltaT_err':SoundAmp_err,\n",
    "                    'DeltaT/g':SoundAmp/gamp,'DeltaT/g_err':SoundAmp_err/gamp,'Gamma':Gamma,'Gamma_err':Gamma_err})\n",
    "df_out.to_clipboard(index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "30px",
    "width": "252px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "551px",
    "left": "0px",
    "right": "1921.34px",
    "top": "106px",
    "width": "267px"
   },
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
